# Copyright 2015 The TensorFlow Authors. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

"""Parsing Ops."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import collections

import re

from tensorflow.python.framework import constant_op

from tensorflow.python.framework import dtypes

from tensorflow.python.framework import ops

from tensorflow.python.framework import sparse_tensor

from tensorflow.python.framework import tensor_shape

from tensorflow.python.ops import array_ops

from tensorflow.python.ops import control_flow_ops

from tensorflow.python.ops import gen_parsing_ops

from tensorflow.python.ops import math_ops

from tensorflow.python.ops import sparse_ops

# go/tf-wildcard-import

# pylint: disable=wildcard-import,undefined-variable

from tensorflow.python.ops.gen_parsing_ops import *

# pylint: enable=wildcard-import,undefined-variable

from tensorflow.python.platform import tf_logging

ops.NotDifferentiable("DecodeRaw")

ops.NotDifferentiable("ParseTensor")

ops.NotDifferentiable("SerializeTensor")

ops.NotDifferentiable("StringToNumber")

class VarLenFeature(collections.namedtuple("VarLenFeature", ["dtype"])):

"""Configuration for parsing a variable-length input feature.

Fields:

dtype: Data type of input.

"""

pass

class SparseFeature(

collections.namedtuple(

"SparseFeature",

["index_key", "value_key", "dtype", "size", "already_sorted"])):

"""Configuration for parsing a sparse input feature from an `Example`.

Note, preferably use `VarLenFeature` (possibly in combination with a

`SequenceExample`) in order to parse out `SparseTensor`s instead of

`SparseFeature` due to its simplicity.

Closely mimicking the `SparseTensor` that will be obtained by parsing an

`Example` with a `SparseFeature` config, a `SparseFeature` contains a

* `value_key`: The name of key for a `Feature` in the `Example` whose parsed

`Tensor` will be the resulting `SparseTensor.values`.

* `index_key`: A list of names - one for each dimension in the resulting

`SparseTensor` whose `indices[i][dim]` indicating the position of

the `i`-th value in the `dim` dimension will be equal to the `i`-th value in

the Feature with key named `index_key[dim]` in the `Example`.

* `size`: A list of ints for the resulting `SparseTensor.dense_shape`.

For example, we can represent the following 2D `SparseTensor`

```python

SparseTensor(indices=[[3, 1], [20, 0]],

values=[0.5, -1.0]

dense_shape=[100, 3])

```

with an `Example` input proto

```python

features {

feature { key: "val" value { float_list { value: [ 0.5, -1.0 ] } } }

feature { key: "ix0" value { int64_list { value: [ 3, 20 ] } } }

feature { key: "ix1" value { int64_list { value: [ 1, 0 ] } } }

}

```

and `SparseFeature` config with 2 `index_key`s

```python

SparseFeature(index_key=["ix0", "ix1"],

value_key="val",

dtype=tf.float32,

size=[100, 3])

```

Fields:

index_key: A single string name or a list of string names of index features.

For each key the underlying feature's type must be `int64` and its length

must always match that of the `value_key` feature.

To represent `SparseTensor`s with a `dense_shape` of `rank` higher than 1

a list of length `rank` should be used.

value_key: Name of value feature. The underlying feature's type must

be `dtype` and its length must always match that of all the `index_key`s'

features.

dtype: Data type of the `value_key` feature.

size: A Python int or list thereof specifying the dense shape. Should be a

list if and only if `index_key` is a list. In that case the list must be

equal to the length of `index_key`. Each for each entry `i` all values in

the `index_key`[i] feature must be in `[0, size[i])`.

already_sorted: A Python boolean to specify whether the values in

`value_key` are already sorted by their index position. If so skip

sorting. False by default (optional).

"""

def __new__(cls, index_key, value_key, dtype, size, already_sorted=False):

return super(SparseFeature, cls).__new__(

cls, index_key, value_key, dtype, size, already_sorted)

class FixedLenFeature(collections.namedtuple(

"FixedLenFeature", ["shape", "dtype", "default_value"])):

"""Configuration for parsing a fixed-length input feature.

To treat sparse input as dense, provide a `default_value`; otherwise,

the parse functions will fail on any examples missing this feature.

Fields:

shape: Shape of input data.

dtype: Data type of input.

default_value: Value to be used if an example is missing this feature. It

must be compatible with `dtype` and of the specified `shape`.

"""

def __new__(cls, shape, dtype, default_value=None):

return super(FixedLenFeature, cls).__new__(

cls, shape, dtype, default_value)

class FixedLenSequenceFeature(collections.namedtuple(

"FixedLenSequenceFeature",

["shape", "dtype", "allow_missing", "default_value"])):

"""Configuration for parsing a variable-length input feature into a `Tensor`.

The resulting `Tensor` of parsing a single `SequenceExample` or `Example` has

a static `shape` of `[None] + shape` and the specified `dtype`.

The resulting `Tensor` of parsing a `batch_size` many `Example`s has

a static `shape` of `[batch_size, None] + shape` and the specified `dtype`.

The entries in the `batch` from different `Examples` will be padded with

`default_value` to the maximum length present in the `batch`.

To treat a sparse input as dense, provide `allow_missing=True`; otherwise,

the parse functions will fail on any examples missing this feature.

Fields:

shape: Shape of input data for dimension 2 and higher. First dimension is

of variable length `None`.

dtype: Data type of input.

allow_missing: Whether to allow this feature to be missing from a feature

list item. Is available only for parsing `SequenceExample` not for

parsing `Examples`.

default_value: Scalar value to be used to pad multiple `Example`s to their

maximum length. Irrelevant for parsing a single `Example` or

`SequenceExample`. Defaults to "" for dtype string and 0 otherwise

(optional).

"""

def __new__(cls, shape, dtype, allow_missing=False, default_value=None):

return super(FixedLenSequenceFeature, cls).__new__(

cls, shape, dtype, allow_missing, default_value)

def _features_to_raw_params(features, types):

"""Split feature tuples into raw params used by `gen_parsing_ops`.

Args:

features: A `dict` mapping feature keys to objects of a type in `types`.

types: Type of features to allow, among `FixedLenFeature`, `VarLenFeature`,

`SparseFeature`, and `FixedLenSequenceFeature`.

Returns:

Tuple of `sparse_keys`, `sparse_types`, `dense_keys`, `dense_types`,

`dense_defaults`, `dense_shapes`.

Raises:

ValueError: if `features` contains an item not in `types`, or an invalid

feature.

"""

sparse_keys = []

sparse_types = []

dense_keys = []

dense_types = []

# When the graph is built twice, multiple dense_defaults in a normal dict

# could come out in different orders. This will fail the _e2e_test which

# expects exactly the same graph.

# OrderedDict which preserves the order can solve the problem.

dense_defaults = collections.OrderedDict()

dense_shapes = []

if features:

# NOTE: We iterate over sorted keys to keep things deterministic.

for key in sorted(features.keys()):

feature = features[key]

if isinstance(feature, VarLenFeature):

if VarLenFeature not in types:

raise ValueError("Unsupported VarLenFeature %s." % feature)

if not feature.dtype:

raise ValueError("Missing type for feature %s." % key)

sparse_keys.append(key)

sparse_types.append(feature.dtype)

elif isinstance(feature, SparseFeature):

if SparseFeature not in types:

raise ValueError("Unsupported SparseFeature %s." % feature)

if not feature.index_key:

raise ValueError(

"Missing index_key for SparseFeature %s." % feature)

if not feature.value_key:

raise ValueError(

"Missing value_key for SparseFeature %s." % feature)

if not feature.dtype:

raise ValueError("Missing type for feature %s." % key)

index_keys = feature.index_key

if isinstance(index_keys, str):

index_keys = [index_keys]

elif len(index_keys) > 1:

tf_logging.warning("SparseFeature is a complicated feature config "

"and should only be used after careful "

"consideration of VarLenFeature.")

for index_key in sorted(index_keys):

if index_key in sparse_keys:

dtype = sparse_types[sparse_keys.index(index_key)]

if dtype != dtypes.int64:

raise ValueError("Conflicting type %s vs int64 for feature %s." %

(dtype, index_key))

else:

sparse_keys.append(index_key)

sparse_types.append(dtypes.int64)

if feature.value_key in sparse_keys:

dtype = sparse_types[sparse_keys.index(feature.value_key)]

if dtype != feature.dtype:

raise ValueError("Conflicting type %s vs %s for feature %s." % (

dtype, feature.dtype, feature.value_key))

else:

sparse_keys.append(feature.value_key)

sparse_types.append(feature.dtype)

elif isinstance(feature, FixedLenFeature):

if FixedLenFeature not in types:

raise ValueError("Unsupported FixedLenFeature %s." % feature)

if not feature.dtype:

raise ValueError("Missing type for feature %s." % key)

if feature.shape is None:

raise ValueError("Missing shape for feature %s." % key)

feature_tensor_shape = tensor_shape.as_shape(feature.shape)

if (feature.shape and feature_tensor_shape.ndims and

feature_tensor_shape.dims[0].value is None):

raise ValueError("First dimension of shape for feature %s unknown. "

"Consider using FixedLenSequenceFeature." % key)

if (feature.shape is not None and

not feature_tensor_shape.is_fully_defined()):

raise ValueError("All dimensions of shape for feature %s need to be "

"known but received %s." % (key, str(feature.shape)))

dense_keys.append(key)

dense_shapes.append(feature.shape)

dense_types.append(feature.dtype)

if feature.default_value is not None:

dense_defaults[key] = feature.default_value

elif isinstance(feature, FixedLenSequenceFeature):

if FixedLenSequenceFeature not in types:

raise ValueError("Unsupported FixedLenSequenceFeature %s." % feature)

if not feature.dtype:

raise ValueError("Missing type for feature %s." % key)

if feature.shape is None:

raise ValueError("Missing shape for feature %s." % key)

dense_keys.append(key)

dense_shapes.append(feature.shape)

dense_types.append(feature.dtype)

if feature.allow_missing:

dense_defaults[key] = None

if feature.default_value is not None:

dense_defaults[key] = feature.default_value

else:

raise ValueError("Invalid feature %s:%s." % (key, feature))

return (

sparse_keys, sparse_types, dense_keys, dense_types, dense_defaults,

dense_shapes)

def _construct_sparse_tensors_for_sparse_features(features, tensor_dict):

"""Merges SparseTensors of indices and values of SparseFeatures.

Constructs new dict based on `tensor_dict`. For `SparseFeatures` in the values

of `features` expects their `index_key`s and `index_value`s to be present in

`tensor_dict` mapping to `SparseTensor`s. Constructs a single `SparseTensor`

from them, and adds it to the result with the key from `features`.

Copies other keys and values from `tensor_dict` with keys present in

`features`.

Args:

features: A `dict` mapping feature keys to `SparseFeature` values.

Values of other types will be ignored.

tensor_dict: A `dict` mapping feature keys to `Tensor` and `SparseTensor`

values. Expected to contain keys of the `SparseFeature`s' `index_key`s and

`value_key`s and mapping them to `SparseTensor`s.

Returns:

A `dict` mapping feature keys to `Tensor` and `SparseTensor` values. Similar

to `tensor_dict` except each `SparseFeature`s in `features` results in a

single `SparseTensor`.

"""

tensor_dict = dict(tensor_dict) # Do not modify argument passed in.

# Construct SparseTensors for SparseFeatures.

for key in sorted(features.keys()):

feature = features[key]

if isinstance(feature, SparseFeature):

if isinstance(feature.index_key, str):

sp_ids = tensor_dict[feature.index_key]

else:

sp_ids = [tensor_dict[index_key] for index_key in feature.index_key]

sp_values = tensor_dict[feature.value_key]

tensor_dict[key] = sparse_ops.sparse_merge(

sp_ids,

sp_values,

vocab_size=feature.size,

already_sorted=feature.already_sorted)

# Remove tensors from dictionary that were only used to construct

# SparseTensors for SparseFeature.

for key in set(tensor_dict) - set(features):

del tensor_dict[key]

return tensor_dict

def _prepend_none_dimension(features):

if features:

modified_features = dict(features) # Create a copy to modify

for key, feature in features.items():

if isinstance(feature, FixedLenSequenceFeature):

if not feature.allow_missing:

raise ValueError("Unsupported: FixedLenSequenceFeature requires "

"allow_missing to be True.")

modified_features[key] = FixedLenSequenceFeature(

[None] + list(feature.shape),

feature.dtype,

feature.allow_missing,

feature.default_value)

return modified_features

else:

return features

def parse_example(serialized, features, name=None, example_names=None):

# pylint: disable=line-too-long

"""Parses `Example` protos into a `dict` of tensors.

Parses a number of serialized [`Example`](https://www.tensorflow.org/code/tensorflow/core/example/example.proto)

protos given in `serialized`. We refer to `serialized` as a batch with

`batch_size` many entries of individual `Example` protos.

`example_names` may contain descriptive names for the corresponding serialized

protos. These may be useful for debugging purposes, but they have no effect on

the output. If not `None`, `example_names` must be the same length as

`serialized`.

This op parses serialized examples into a dictionary mapping keys to `Tensor`

and `SparseTensor` objects. `features` is a dict from keys to `VarLenFeature`,

`SparseFeature`, and `FixedLenFeature` objects. Each `VarLenFeature`

and `SparseFeature` is mapped to a `SparseTensor`, and each

`FixedLenFeature` is mapped to a `Tensor`.

Each `VarLenFeature` maps to a `SparseTensor` of the specified type

representing a ragged matrix. Its indices are `[batch, index]` where `batch`

identifies the example in `serialized`, and `index` is the value's index in

the list of values associated with that feature and example.

Each `SparseFeature` maps to a `SparseTensor` of the specified type

representing a Tensor of `dense_shape` `[batch_size] + SparseFeature.size`.

Its `values` come from the feature in the examples with key `value_key`.

A `values[i]` comes from a position `k` in the feature of an example at batch

entry `batch`. This positional information is recorded in `indices[i]` as

`[batch, index_0, index_1, ...]` where `index_j` is the `k-th` value of

the feature in the example at with key `SparseFeature.index_key[j].

In other words, we split the indices (except the first index indicating the

batch entry) of a `SparseTensor` by dimension into different features of the

`Example`. Due to its complexity a `VarLenFeature` should be preferred over a

`SparseFeature` whenever possible.

Each `FixedLenFeature` `df` maps to a `Tensor` of the specified type (or

`tf.float32` if not specified) and shape `(serialized.size(),) + df.shape`.

`FixedLenFeature` entries with a `default_value` are optional. With no default

value, we will fail if that `Feature` is missing from any example in

`serialized`.

Each `FixedLenSequenceFeature` `df` maps to a `Tensor` of the specified type

(or `tf.float32` if not specified) and shape

`(serialized.size(), None) + df.shape`.

All examples in `serialized` will be padded with `default_value` along the

second dimension.

Examples:

For example, if one expects a `tf.float32` `VarLenFeature` `ft` and three

serialized `Example`s are provided:

```

serialized = [

features

{ feature { key: "ft" value { float_list { value: [1.0, 2.0] } } } },

features

{ feature []},

features

{ feature { key: "ft" value { float_list { value: [3.0] } } }

]

```

then the output will look like:

```python

{"ft": SparseTensor(indices=[[0, 0], [0, 1], [2, 0]],

values=[1.0, 2.0, 3.0],

dense_shape=(3, 2)) }

```

If instead a `FixedLenSequenceFeature` with `default_value = -1.0` and

`shape=[]` is used then the output will look like:

```python

{"ft": [[1.0, 2.0], [3.0, -1.0]]}

```

Given two `Example` input protos in `serialized`:

```

[

features {

feature { key: "kw" value { bytes_list { value: [ "knit", "big" ] } } }

feature { key: "gps" value { float_list { value: [] } } }

},

features {

feature { key: "kw" value { bytes_list { value: [ "emmy" ] } } }

feature { key: "dank" value { int64_list { value: [ 42 ] } } }

feature { key: "gps" value { } }

}

]

```

And arguments

```

example_names: ["input0", "input1"],

features: {

"kw": VarLenFeature(tf.string),

"dank": VarLenFeature(tf.int64),

"gps": VarLenFeature(tf.float32),

}

```

Then the output is a dictionary:

```python

{

"kw": SparseTensor(

indices=[[0, 0], [0, 1], [1, 0]],

values=["knit", "big", "emmy"]

dense_shape=[2, 2]),

"dank": SparseTensor(

indices=[[1, 0]],

values=[42],

dense_shape=[2, 1]),

"gps": SparseTensor(

indices=[],

values=[],

dense_shape=[2, 0]),

}

```

For dense results in two serialized `Example`s:

```

[

features {

feature { key: "age" value { int64_list { value: [ 0 ] } } }

feature { key: "gender" value { bytes_list { value: [ "f" ] } } }

},

features {

feature { key: "age" value { int64_list { value: [] } } }

feature { key: "gender" value { bytes_list { value: [ "f" ] } } }

}

]

```

We can use arguments:

```

example_names: ["input0", "input1"],

features: {

"age": FixedLenFeature([], dtype=tf.int64, default_value=-1),

"gender": FixedLenFeature([], dtype=tf.string),

}

```

And the expected output is:

```python

{

"age": [[0], [-1]],

"gender": [["f"], ["f"]],

}

```

An alternative to `VarLenFeature` to obtain a `SparseTensor` is

`SparseFeature`. For example, given two `Example` input protos in

`serialized`:

```

[

features {

feature { key: "val" value { float_list { value: [ 0.5, -1.0 ] } } }

feature { key: "ix" value { int64_list { value: [ 3, 20 ] } } }

},

features {

feature { key: "val" value { float_list { value: [ 0.0 ] } } }

feature { key: "ix" value { int64_list { value: [ 42 ] } } }

}

]

```

And arguments

```

example_names: ["input0", "input1"],

features: {

"sparse": SparseFeature(

index_key="ix", value_key="val", dtype=tf.float32, size=100),

}

```

Then the output is a dictionary:

```python

{

"sparse": SparseTensor(

indices=[[0, 3], [0, 20], [1, 42]],

values=[0.5, -1.0, 0.0]

dense_shape=[2, 100]),

}

```

Args:

serialized: A vector (1-D Tensor) of strings, a batch of binary

serialized `Example` protos.

features: A `dict` mapping feature keys to `FixedLenFeature`,

`VarLenFeature`, and `SparseFeature` values.

name: A name for this operation (optional).

example_names: A vector (1-D Tensor) of strings (optional), the names of

the serialized protos in the batch.

Returns:

A `dict` mapping feature keys to `Tensor` and `SparseTensor` values.

Raises:

ValueError: if any feature is invalid.

"""

if not features:

raise ValueError("Missing: features was %s." % features)

features = _prepend_none_dimension(features)

(sparse_keys, sparse_types, dense_keys, dense_types, dense_defaults,

dense_shapes) = _features_to_raw_params(

features,

[VarLenFeature, SparseFeature, FixedLenFeature, FixedLenSequenceFeature])

outputs = _parse_example_raw(

serialized, example_names, sparse_keys, sparse_types, dense_keys,

dense_types, dense_defaults, dense_shapes, name)

return _construct_sparse_tensors_for_sparse_features(features, outputs)

def _parse_example_raw(serialized,

names=None,

sparse_keys=None,

sparse_types=None,

dense_keys=None,

dense_types=None,

dense_defaults=None,

dense_shapes=None,

name=None):

"""Parses `Example` protos.

Args:

serialized: A vector (1-D Tensor) of strings, a batch of binary

serialized `Example` protos.

names: A vector (1-D Tensor) of strings (optional), the names of

the serialized protos.

sparse_keys: A list of string keys in the examples' features.

The results for these keys will be returned as `SparseTensor` objects.

sparse_types: A list of `DTypes` of the same length as `sparse_keys`.

Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`),

and `tf.string` (`BytesList`) are supported.

dense_keys: A list of string keys in the examples' features.

The results for these keys will be returned as `Tensor`s

dense_types: A list of DTypes of the same length as `dense_keys`.

Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`),

and `tf.string` (`BytesList`) are supported.

dense_defaults: A dict mapping string keys to `Tensor`s.

The keys of the dict must match the dense_keys of the feature.

dense_shapes: A list of tuples with the same length as `dense_keys`.

The shape of the data for each dense feature referenced by `dense_keys`.

Required for any input tensors identified by `dense_keys`. Must be

either fully defined, or may contain an unknown first dimension.

An unknown first dimension means the feature is treated as having

a variable number of blocks, and the output shape along this dimension

is considered unknown at graph build time. Padding is applied for

minibatch elements smaller than the maximum number of blocks for the

given feature along this dimension.

name: A name for this operation (optional).

Returns:

A `dict` mapping keys to `Tensor`s and `SparseTensor`s.

Raises:

ValueError: If sparse and dense key sets intersect, or input lengths do not

match up.

"""

with ops.name_scope(name, "ParseExample", [serialized, names]):

names = [] if names is None else names

dense_defaults = collections.OrderedDict(

) if dense_defaults is None else dense_defaults

sparse_keys = [] if sparse_keys is None else sparse_keys

sparse_types = [] if sparse_types is None else sparse_types

dense_keys = [] if dense_keys is None else dense_keys

dense_types = [] if dense_types is None else dense_types

dense_shapes = (

[[]] * len(dense_keys) if dense_shapes is None else dense_shapes)

num_dense = len(dense_keys)

num_sparse = len(sparse_keys)

if len(dense_shapes) != num_dense:

raise ValueError("len(dense_shapes) != len(dense_keys): %d vs. %d"

% (len(dense_shapes), num_dense))

if len(dense_types) != num_dense:

raise ValueError("len(dense_types) != len(num_dense): %d vs. %d"

% (len(dense_types), num_dense))

if len(sparse_types) != num_sparse:

raise ValueError("len(sparse_types) != len(sparse_keys): %d vs. %d"

% (len(sparse_types), num_sparse))

if num_dense + num_sparse == 0:

raise ValueError("Must provide at least one sparse key or dense key")

if not set(dense_keys).isdisjoint(set(sparse_keys)):

raise ValueError(

"Dense and sparse keys must not intersect; intersection: %s" %

set(dense_keys).intersection(set(sparse_keys)))

# Convert dense_shapes to TensorShape object.

dense_shapes = [tensor_shape.as_shape(shape) for shape in dense_shapes]

dense_defaults_vec = []

for i, key in enumerate(dense_keys):

default_value = dense_defaults.get(key)

dense_shape = dense_shapes[i]

if (dense_shape.ndims is not None and dense_shape.ndims > 0 and

dense_shape[0].value is None):

# Variable stride dense shape, the default value should be a

# scalar padding value

if default_value is None:

default_value = ops.convert_to_tensor(

"" if dense_types[i] == dtypes.string else 0,

dtype=dense_types[i])

else:

# Reshape to a scalar to ensure user gets an error if they

# provide a tensor that's not intended to be a padding value

# (0 or 2+ elements).

key_name = "padding_" + re.sub("[^A-Za-z0-9_.\\-/]", "_", key)

default_value = ops.convert_to_tensor(

default_value, dtype=dense_types[i], name=key_name)

default_value = array_ops.reshape(default_value, [])

else:

if default_value is None:

default_value = constant_op.constant([], dtype=dense_types[i])

elif not isinstance(default_value, ops.Tensor):

key_name = "key_" + re.sub("[^A-Za-z0-9_.\\-/]", "_", key)

default_value = ops.convert_to_tensor(

default_value, dtype=dense_types[i], name=key_name)

default_value = array_ops.reshape(default_value, dense_shape)

dense_defaults_vec.append(default_value)

# Finally, convert dense_shapes to TensorShapeProto

dense_shapes = [shape.as_proto() for shape in dense_shapes]

# pylint: disable=protected-access

outputs = gen_parsing_ops._parse_example(

serialized=serialized,

names=names,

dense_defaults=dense_defaults_vec,

sparse_keys=sparse_keys,

sparse_types=sparse_types,

dense_keys=dense_keys,

dense_shapes=dense_shapes,

name=name)

# pylint: enable=protected-access

(sparse_indices, sparse_values, sparse_shapes, dense_values) = outputs

sparse_tensors = [

sparse_tensor.SparseTensor(ix, val, shape) for (ix, val, shape)

in zip(sparse_indices, sparse_values, sparse_shapes)]

return dict(zip(sparse_keys + dense_keys, sparse_tensors + dense_values))

def parse_single_example(serialized, features, name=None, example_names=None):

"""Parses a single `Example` proto.

Similar to `parse_example`, except:

For dense tensors, the returned `Tensor` is identical to the output of

`parse_example`, except there is no batch dimension, the output shape is the

same as the shape given in `dense_shape`.

For `SparseTensor`s, the first (batch) column of the indices matrix is removed

(the indices matrix is a column vector), the values vector is unchanged, and

the first (`batch_size`) entry of the shape vector is removed (it is now a

single element vector).

One might see performance advantages by batching `Example` protos with

`parse_example` instead of using this function directly.

Args:

serialized: A scalar string Tensor, a single serialized Example.

See `_parse_single_example_raw` documentation for more details.

features: A `dict` mapping feature keys to `FixedLenFeature` or

`VarLenFeature` values.

name: A name for this operation (optional).

example_names: (Optional) A scalar string Tensor, the associated name.

See `_parse_single_example_raw` documentation for more details.

Returns:

A `dict` mapping feature keys to `Tensor` and `SparseTensor` values.

Raises:

ValueError: if any feature is invalid.

"""

if not features:

raise ValueError("Missing features.")

features = _prepend_none_dimension(features)

(sparse_keys, sparse_types, dense_keys, dense_types, dense_defaults,

dense_shapes) = _features_to_raw_params(

features,

[VarLenFeature, FixedLenFeature, FixedLenSequenceFeature, SparseFeature])

outputs = _parse_single_example_raw(

serialized, example_names, sparse_keys, sparse_types, dense_keys,

dense_types, dense_defaults, dense_shapes, name)

return _construct_sparse_tensors_for_sparse_features(features, outputs)

def _parse_single_example_raw(serialized,

names=None,

sparse_keys=None,

sparse_types=None,

dense_keys=None,

dense_types=None,

dense_defaults=None,

dense_shapes=None,

name=None):

"""Parses a single `Example` proto.

Args:

serialized: A scalar string Tensor, a single serialized Example.

See `_parse_example_raw` documentation for more details.

names: (Optional) A scalar string Tensor, the associated name.

See `_parse_example_raw` documentation for more details.

sparse_keys: See `_parse_example_raw` documentation for more details.

sparse_types: See `_parse_example_raw` documentation for more details.

dense_keys: See `_parse_example_raw` documentation for more details.

dense_types: See `_parse_example_raw` documentation for more details.

dense_defaults: See `_parse_example_raw` documentation for more details.

dense_shapes: See `_parse_example_raw` documentation for more details.

name: A name for this operation (optional).

Returns:

A `dict` mapping feature keys to `Tensor` and `SparseTensor` values.

Raises:

ValueError: if any feature is invalid.

"""

with ops.name_scope(name, "ParseSingleExample", [serialized, names]):

serialized = ops.convert_to_tensor(serialized)

serialized_shape = serialized.get_shape()

if serialized_shape.ndims is not None:

if serialized_shape.ndims != 0:

raise ValueError("Input serialized must be a scalar")

else:

serialized = control_flow_ops.with_dependencies(

[control_flow_ops.Assert(

math_ops.equal(array_ops.rank(serialized), 0),

["Input serialized must be a scalar"],

name="SerializedIsScalar")],

serialized,

name="SerializedDependencies")

serialized = array_ops.expand_dims(serialized, 0)

if names is not None:

names = ops.convert_to_tensor(names)

names_shape = names.get_shape()

if names_shape.ndims is not None:

if names_shape.ndims != 0:

raise ValueError("Input names must be a scalar")

else:

names = control_flow_ops.with_dependencies(

[control_flow_ops.Assert(

math_ops.equal(array_ops.rank(names), 0),

["Input names must be a scalar"],

name="NamesIsScalar")],

names,

name="NamesDependencies")

names = array_ops.expand_dims(names, 0)

outputs = _parse_example_raw(

serialized,

names=names,

sparse_keys=sparse_keys,

sparse_types=sparse_types,

dense_keys=dense_keys,

dense_types=dense_types,

dense_defaults=dense_defaults,

dense_shapes=dense_shapes,

name=name)

if dense_keys is not None:

for d in dense_keys:

d_name = re.sub("[^A-Za-z0-9_.\\-/]", "_", d)

outputs[d] = array_ops.squeeze(

outputs[d], [0], name="Squeeze_%s" % d_name)

if sparse_keys is not None:

for s in sparse_keys:

s_name = re.sub("[^A-Za-z0-9_.\\-/]", "_", s)

outputs[s] = sparse_tensor.SparseTensor(

array_ops.slice(outputs[s].indices,

[0, 1], [-1, -1], name="Slice_Indices_%s" % s_name),

outputs[s].values,

array_ops.slice(outputs[s].dense_shape,

[1], [-1], name="Squeeze_Shape_%s" % s_name))

return outputs

def parse_single_sequence_example(

serialized, context_features=None, sequence_features=None,

example_name=None, name=None):

# pylint: disable=line-too-long

"""Parses a single `SequenceExample` proto.

Parses a single serialized [`SequenceExample`](https://www.tensorflow.org/code/tensorflow/core/example/example.proto)

proto given in `serialized`.

This op parses a serialized sequence example into a tuple of dictionaries

mapping keys to `Tensor` and `SparseTensor` objects respectively.

The first dictionary contains mappings for keys appearing in

`context_features`, and the second dictionary contains mappings for keys

appearing in `sequence_features`.

At least one of `context_features` and `sequence_features` must be provided

and non-empty.

The `context_features` keys are associated with a `SequenceExample` as a

whole, independent of time / frame. In contrast, the `sequence_features` keys

provide a way to access variable-length data within the `FeatureList` section

of the `SequenceExample` proto. While the shapes of `context_features` values

are fixed with respect to frame, the frame dimension (the first dimension)

of `sequence_features` values may vary between `SequenceExample` protos,

and even between `feature_list` keys within the same `SequenceExample`.

`context_features` contains `VarLenFeature` and `FixedLenFeature` objects.

Each `VarLenFeature` is mapped to a `SparseTensor`, and each `FixedLenFeature`

is mapped to a `Tensor`, of the specified type, shape, and default value.

`sequence_features` contains `VarLenFeature` and `FixedLenSequenceFeature`

objects. Each `VarLenFeature` is mapped to a `SparseTensor`, and each

`FixedLenSequenceFeature` is mapped to a `Tensor`, each of the specified type.

The shape will be `(T,) + df.dense_shape` for `FixedLenSequenceFeature` `df`, where

`T` is the length of the associated `FeatureList` in the `SequenceExample`.

For instance, `FixedLenSequenceFeature([])` yields a scalar 1-D `Tensor` of

static shape `[None]` and dynamic shape `[T]`, while

`FixedLenSequenceFeature([k])` (for `int k >= 1`) yields a 2-D matrix `Tensor`

of static shape `[None, k]` and dynamic shape `[T, k]`.

Each `SparseTensor` corresponding to `sequence_features` represents a ragged

vector. Its indices are `[time, index]`, where `time` is the `FeatureList`

entry and `index` is the value's index in the list of values associated with

that time.

`FixedLenFeature` entries with a `default_value` and `FixedLenSequenceFeature`

entries with `allow_missing=True` are optional; otherwise, we will fail if

that `Feature` or `FeatureList` is missing from any example in `serialized`.

`example_name` may contain a descriptive name for the corresponding serialized

proto. This may be useful for debugging purposes, but it has no effect on the

output. If not `None`, `example_name` must be a scalar.

Args:

serialized: A scalar (0-D Tensor) of type string, a single binary

serialized `SequenceExample` proto.

context_features: A `dict` mapping feature keys to `FixedLenFeature` or

`VarLenFeature` values. These features are associated with a

`SequenceExample` as a whole.

sequence_features: A `dict` mapping feature keys to

`FixedLenSequenceFeature` or `VarLenFeature` values. These features are

associated with data within the `FeatureList` section of the

`SequenceExample` proto.

example_name: A scalar (0-D Tensor) of strings (optional), the name of

the serialized proto.

name: A name for this operation (optional).

Returns:

A tuple of two `dict`s, each mapping keys to `Tensor`s and `SparseTensor`s.

The first dict contains the context key/values.

The second dict contains the feature_list key/values.

Raises:

ValueError: if any feature is invalid.

"""

# pylint: enable=line-too-long

if not (context_features or sequence_features):

raise ValueError("Missing features.")

(context_sparse_keys, context_sparse_types, context_dense_keys,

context_dense_types, context_dense_defaults,

context_dense_shapes) = _features_to_raw_params(

context_features, [VarLenFeature, FixedLenFeature])

(feature_list_sparse_keys, feature_list_sparse_types,

feature_list_dense_keys, feature_list_dense_types,

feature_list_dense_defaults,

feature_list_dense_shapes) = _features_to_raw_params(

sequence_features, [VarLenFeature, FixedLenSequenceFeature])

return _parse_single_sequence_example_raw(

serialized, context_sparse_keys, context_sparse_types,

context_dense_keys, context_dense_types, context_dense_defaults,

context_dense_shapes, feature_list_sparse_keys,

feature_list_sparse_types, feature_list_dense_keys,

feature_list_dense_types, feature_list_dense_shapes,

feature_list_dense_defaults, example_name, name)

def _parse_single_sequence_example_raw(serialized,

context_sparse_keys=None,

context_sparse_types=None,

context_dense_keys=None,

context_dense_types=None,

context_dense_defaults=None,

context_dense_shapes=None,

feature_list_sparse_keys=None,

feature_list_sparse_types=None,

feature_list_dense_keys=None,

feature_list_dense_types=None,

feature_list_dense_shapes=None,

feature_list_dense_defaults=None,

debug_name=None,

name=None):

"""Parses a single `SequenceExample` proto.

Args:

serialized: A scalar (0-D Tensor) of type string, a single binary

serialized `SequenceExample` proto.

context_sparse_keys: A list of string keys in the `SequenceExample`'s

features. The results for these keys will be returned as

`SparseTensor` objects.

context_sparse_types: A list of `DTypes`, the same length as `sparse_keys`.

Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`),

and `tf.string` (`BytesList`) are supported.

context_dense_keys: A list of string keys in the examples' features.

The results for these keys will be returned as `Tensor`s

context_dense_types: A list of DTypes, same length as `context_dense_keys`.

Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`),

and `tf.string` (`BytesList`) are supported.

context_dense_defaults: A dict mapping string keys to `Tensor`s.

The keys of the dict must match the context_dense_keys of the feature.

context_dense_shapes: A list of tuples, same length as `context_dense_keys`.

The shape of the data for each context_dense feature referenced by

`context_dense_keys`. Required for any input tensors identified by

`context_dense_keys` whose shapes are anything other than `[]` or `[1]`.

feature_list_sparse_keys: A list of string keys in the `SequenceExample`'s

feature_lists. The results for these keys will be returned as

`SparseTensor` objects.

feature_list_sparse_types: A list of `DTypes`, same length as `sparse_keys`.

Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`),

and `tf.string` (`BytesList`) are supported.

feature_list_dense_keys: A list of string keys in the `SequenceExample`'s

features_lists. The results for these keys will be returned as `Tensor`s.

feature_list_dense_types: A list of `DTypes`, same length as

`feature_list_dense_keys`. Only `tf.float32` (`FloatList`),

`tf.int64` (`Int64List`), and `tf.string` (`BytesList`) are supported.

feature_list_dense_shapes: A list of tuples, same length as

`feature_list_dense_keys`. The shape of the data for each

`FeatureList` feature referenced by `feature_list_dense_keys`.

feature_list_dense_defaults: A dict mapping key strings to values.

The only currently allowed value is `None`. Any key appearing

in this dict with value `None` is allowed to be missing from the